Pushing the Exoplanet Frontier with Deep Learning

This summer I was invited to take part in the 2018 NASA Frontier Development Lab, along with a small team including Michele Sasdelli (University of Adelaide), and a pair of planetary scientists, Megan Ansdel (University of California at Berkeley) and Hugh Osborn (Laboratoire d'Astrophysique de Marseille). Our team composed of both machine learning and planetary scientists, was challenged over the course of 8 weeks to combine our expert knowledge in order to improve the methods behind one of the most exciting frontiers of science: exoplanet discovery. Here I discuss some of the challenges of applying machine learning to real-world scientific data, in particular noisy and sparse periodic time-series data. Our knowledge of exoplanets, or planets that exist outside our Solar System, has advanced drastically over the last few decades. In fact, until relatively recently one could have called exoplanets a theoretical concept.